CDC Daily by State

Background

This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.

Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.

The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project.

The code in this module builds on code available in _v005, with function and mapping files updated:

Generic_Added_Utility_Functions_202105_v001.R - generic functions that can be used in other areas
Coronavirus_CDC_Daily_Functions_v002.R - functions specific to coronavirus daily data

Broadly, the CDC data analyzed by this module includes:

CDC weekly case and death data by state and date are available for download on the CDC website
CDC hospital data are available for download on the healthdata.gov website
CDC vaccines data are also available for download on the CDC website:

Functions and Mapping Files

The tidyverse package is loaded and functions are sourced:

# The tidyverse functions are routinely used without package::function format
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.1     ✔ tibble    3.1.8
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(geofacet)

# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")

A series of mapping files are also available to allow for parameterized processing. Mappings include:

urlMapper - mapping file for urlType and url location to download data
renMapper - mapping file for renaming of variables in the raw data file
selfListMapper - mapping file for transformations by variable type
fullListMapper - mapping file for transformations across variable types
lstComboMapper - mapping file for elements to be combined by data type (most common is to combine NYC and NYS data to NY if the file provides them separately)
uqMapper - mapping file for fields that should combine to be unique keys for processed files
lstFilterMapper - mapping file for filtering to subset (most common is to keep 50 states and DC)
vecSelectMapper - mapping file for variables to keep
checkControlGroupMapper - mapping file for group_by() of control total checks
checkControlAggMapper - mapping file for numeric variables for control total checks
checkSimilarityMapper - mapping file for similarity checks to perform
plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
keyAggMapper - mapping file for the aggregate-level control total checks to perform
perCapMapper - named vector that drives conversion from original field name to per capita field name
hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file

These default parameters are maintained in a separate .R file and can be sourced:

# These have been updated to _v003
source("./Coronavirus_CDC_Daily_Default_Mappings_v003.R")

Example Code Processing

The function is run on previously downloaded data:

The latest data are downloaded and processed:

readList <- list("cdcWeeklyBurden"="./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230402.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_230402.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_230402.csv"
                 )
compareList <- list("cdcWeeklyBurden"=readFromRDS("cdc_daily_230302")$dfRaw$cdcWeeklyBurden, 
                    "cdcHosp"=readFromRDS("cdc_daily_230302")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_230302")$dfRaw$vax
                    )

cdc_daily_230402 <- readRunCDCDaily(thruLabel="Mar 31, 2023", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )

## 
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_wkly_downloaded_230402.csv

## Rows: 10020 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): date_updated, state, start_date, end_date
## dbl (6): tot_cases, new_cases, tot_deaths, new_deaths, new_historic_cases, n...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `across(.cols = all_of(useVars), .fns = fn, ...)`.
## ℹ In group 1: `date = 2020-01-22`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
## 
##   # Previously
##   across(a:b, mean, na.rm = TRUE)
## 
##   # Now
##   across(a:b, \(x) mean(x, na.rm = TRUE))

## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2021-12-08 new_deaths     6465     8857     2392 0.31223078
## 2  2023-01-11 new_deaths     4108     4448      340 0.07947639
## 3  2022-06-01 new_deaths     1994     2103      109 0.05320967
## 4  2021-07-14  new_cases   251653   178256    73397 0.34145366
## 5  2021-07-07  new_cases   134473   109537    24936 0.20438507
## 6  2023-01-18  new_cases   338956   316627    22329 0.06811952
## 7  2021-06-02  new_cases   120788   113131     7657 0.06546711
## 8  2020-11-04  new_cases   652612   694701    42089 0.06247843
## 9  2020-08-12  new_cases   399531   377082    22449 0.05781258
## 10 2021-08-11  new_cases   851192   901672    50480 0.05759717
## 11 2020-08-19  new_cases   349383   330558    18825 0.05537245
## 12 2023-01-11  new_cases   432643   455887    23244 0.05232012
## 13 2021-06-30  new_cases    98741    93823     4918 0.05107912

## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.

## Warning in left_join(., ref, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     MO tot_deaths   2041320   1889376   151944 0.077311499
## 2     MN tot_deaths   1323494   1311962    11532 0.008751427
## 3     RI tot_deaths    398207    399829     1622 0.004064980
## 4     MO  tot_cases 134077761 135468146  1390385 0.010316499
## 5     FL  tot_cases 556254722 561378228  5123506 0.009168495
## 6     DE  tot_cases  24269412  24152458   116954 0.004830627
## 7     TX  tot_cases 642477094 640839111  1637983 0.002552735
## 8     GU new_deaths       415       420        5 0.011976048
## 9     MO new_deaths     20214     20354      140 0.006901992
## 10    VT new_deaths       930       925        5 0.005390836
## 11    AR new_deaths     12930     12980       50 0.003859514
## 12    MN new_deaths     14448     14402       46 0.003188908
## 13    RI new_deaths      3870      3860       10 0.002587322
## 14    DE new_deaths      3329      3324        5 0.001503081
## 15    OK new_deaths     15411     15432       21 0.001361735
## 16    MO  new_cases   1764510   1773152     8642 0.004885713
## 17    FL  new_cases   7499410   7528420    29010 0.003860837
## 18    DE  new_cases    331763    330817      946 0.002855504
## 19    KY  new_cases   1716242   1713684     2558 0.001491577
## 20    OK  new_cases   1282596   1284450     1854 0.001444462
## 21    MP  new_cases     13680     13666       14 0.001023916
## 
## 
## 
## Raw file for cdcWeeklyBurden:
## Rows: 10,020
## Columns: 10
## $ date_updated        <chr> "01/23/2020", "01/30/2020", "02/06/2020", "02/13/2…
## $ state               <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "A…
## $ start_date          <chr> "01/16/2020", "01/23/2020", "01/30/2020", "02/06/2…
## $ date                <date> 2020-01-22, 2020-01-29, 2020-02-05, 2020-02-12, 2…
## $ tot_cases           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 63, 149, 235, 300, 337…
## $ new_cases           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 11, 52, 86, 86, 65, 37, 18…
## $ tot_deaths          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 9, 9, 9, 10, 1…
## $ new_deaths          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 2, 0, 0, 1, 0,…
## $ new_historic_cases  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_historic_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## 
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_230402.csv

## Rows: 60599 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr    (1): state
## dbl  (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl    (1): geocoded_state
## date   (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date     name newValue refValue absDelta   pctDelta
## 1 2023-03-02 hosp_ped     1143     1038      105 0.09628611

## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     VI        inp     6233     6118      115 0.018621974
## 2     AK        inp    71283    71837      554 0.007741755
## 3     KS        inp   423272   422782      490 0.001158318
## 4     VI   hosp_ped      158      147       11 0.072131148
## 5     AK   hosp_ped     3229     3258       29 0.008940959
## 6     KS   hosp_ped     5987     5969       18 0.003011040
## 7     PR   hosp_ped    27722    27684       38 0.001371693
## 8     NH   hosp_ped     1572     1570        2 0.001273074
## 9     IA   hosp_ped     9618     9606       12 0.001248439
## 10    HI   hosp_ped     4559     4554        5 0.001097333
## 11    WY   hosp_ped      987      988        1 0.001012658
## 12    VI hosp_adult     5910     5806      104 0.017753499
## 13    AK hosp_adult    65959    66484      525 0.007927939
## 14    KS hosp_adult   393875   393403      472 0.001199068
## 15    PR hosp_adult   251141   250889      252 0.001003924
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 60,599
## Columns: 135
## $ state                                                                        <chr> …
## $ date                                                                         <date> …
## $ critical_staffing_shortage_today_yes                                         <dbl> …
## $ critical_staffing_shortage_today_no                                          <dbl> …
## $ critical_staffing_shortage_today_not_reported                                <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> …
## $ hospital_onset_covid                                                         <dbl> …
## $ hospital_onset_covid_coverage                                                <dbl> …
## $ inpatient_beds                                                               <dbl> …
## $ inpatient_beds_coverage                                                      <dbl> …
## $ inpatient_beds_used                                                          <dbl> …
## $ inpatient_beds_used_coverage                                                 <dbl> …
## $ inp                                                                          <dbl> …
## $ inpatient_beds_used_covid_coverage                                           <dbl> …
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> …
## $ previous_day_admission_adult_covid_suspected                                 <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> …
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> …
## $ staffed_adult_icu_bed_occupancy                                              <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> …
## $ hosp_adult                                                                   <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> …
## $ hosp_ped                                                                     <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> …
## $ total_staffed_adult_icu_beds                                                 <dbl> …
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> …
## $ inpatient_beds_utilization                                                   <dbl> …
## $ inpatient_beds_utilization_coverage                                          <dbl> …
## $ inpatient_beds_utilization_numerator                                         <dbl> …
## $ inpatient_beds_utilization_denominator                                       <dbl> …
## $ percent_of_inpatients_with_covid                                             <dbl> …
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> …
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> …
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> …
## $ inpatient_bed_covid_utilization                                              <dbl> …
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> …
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> …
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_covid_utilization                                              <dbl> …
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> …
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> …
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_utilization                                                    <dbl> …
## $ adult_icu_bed_utilization_coverage                                           <dbl> …
## $ adult_icu_bed_utilization_numerator                                          <dbl> …
## $ adult_icu_bed_utilization_denominator                                        <dbl> …
## $ geocoded_state                                                               <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> …
## $ deaths_covid                                                                 <dbl> …
## $ deaths_covid_coverage                                                        <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> …
## $ icu_patients_confirmed_influenza                                             <dbl> …
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> …
## $ previous_day_admission_influenza_confirmed                                   <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> …
## $ previous_day_deaths_covid_and_influenza                                      <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> …
## $ previous_day_deaths_influenza                                                <dbl> …
## $ previous_day_deaths_influenza_coverage                                       <dbl> …
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> …
## $ all_pediatric_inpatient_beds                                                 <dbl> …
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> …
## $ total_staffed_pediatric_icu_beds                                             <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> …
## 
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_230402.csv

## Rows: 38104 Columns: 109
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (2): Date, Location
## dbl (107): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 5
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)

## Warning in left_join(., df, by = names(univData)): Each row in `x` is expected to match at most 1 row in `y`.
## Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 38,104
## Columns: 109
## $ date                                   <date> 2023-03-29, 2023-03-29, 2023-0…
## $ MMWR_week                              <dbl> 13, 13, 13, 13, 13, 13, 13, 13,…
## $ state                                  <chr> "BP2", "AS", "HI", "IH2", "RI",…
## $ Distributed                            <dbl> 418430, 128480, 4598400, 407234…
## $ Distributed_Janssen                    <dbl> 16200, 600, 124900, 111800, 905…
## $ Distributed_Moderna                    <dbl> 172520, 25000, 1503820, 1528780…
## $ Distributed_Pfizer                     <dbl> 198750, 100770, 2362220, 193963…
## $ Distributed_Novavax                    <dbl> 0, 0, 4400, 8300, 4400, 3100, 4…
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K                          <dbl> 0, 271101, 324775, 195521, 3293…
## $ Distributed_Per_100k_5Plus             <dbl> 0, 298028, 345575, 0, 347230, 4…
## $ Distributed_Per_100k_12Plus            <dbl> 0, 348024, 379685, 0, 376187, 4…
## $ Distributed_Per_100k_18Plus            <dbl> 0, 410021, 412042, 0, 408146, 4…
## $ Distributed_Per_100k_65Plus            <dbl> 0, 3919460, 1712960, 0, 1865370…
## $ vxa                                    <dbl> 347647, 115241, 3507351, 255996…
## $ Administered_5Plus                     <dbl> 347645, 115218, 3484460, 254425…
## $ Administered_12Plus                    <dbl> 347645, 102427, 3345431, 243141…
## $ Administered_18Plus                    <dbl> 347645, 85702, 3132541, 2221391…
## $ Administered_65Plus                    <dbl> 11918, 9482, 987470, 478068, 72…
## $ Administered_Janssen                   <dbl> 14831, 580, 71624, 42014, 67173…
## $ Administered_Moderna                   <dbl> 156444, 25359, 1170251, 1098391…
## $ Administered_Pfizer                    <dbl> 166491, 87454, 1952507, 1249079…
## $ Administered_Novavax                   <dbl> 0, 0, 358, 62, 475, 228, 1719, …
## $ Administered_Unk_Manuf                 <dbl> 38, 1280, 1264, 1168, 2958, 566…
## $ Admin_Per_100k                         <dbl> 0, 243166, 247717, 122909, 2510…
## $ Admin_Per_100k_5Plus                   <dbl> 0, 267265, 261861, 0, 262891, 3…
## $ Admin_Per_100k_12Plus                  <dbl> 0, 277452, 276229, 0, 275080, 3…
## $ Admin_Per_100k_18Plus                  <dbl> 0, 273502, 280693, 0, 280703, 3…
## $ Admin_Per_100k_65Plus                  <dbl> 0, 289262, 367844, 0, 389569, 4…
## $ Recip_Administered                     <dbl> 347647, 115689, 3542243, 255996…
## $ Administered_Dose1_Recip               <dbl> 155680, 46206, 1296078, 1164523…
## $ Administered_Dose1_Pop_Pct             <dbl> 0.0, 95.0, 91.5, 55.9, 95.0, 95…
## $ Administered_Dose1_Recip_5Plus         <dbl> 155678, 46185, 1284179, 1155760…
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_12Plus        <dbl> 155678, 39582, 1219812, 1098519…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_18Plus        <dbl> 155678, 32786, 1128879, 998919,…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ Administered_Dose1_Recip_65Plus        <dbl> 4224, 3313, 281004, 187075, 234…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 0.0, 95.0, 95.…
## $ vxc                                    <dbl> 141689, 42479, 1155548, 919151,…
## $ vxcpoppct                              <dbl> 0.0, 89.6, 81.6, 44.1, 87.9, 90…
## $ Series_Complete_5Plus                  <dbl> 141688, 42478, 1149155, 916394,…
## $ Series_Complete_5PlusPop_Pct           <dbl> 0.0, 95.0, 86.4, 0.0, 92.1, 95.…
## $ Series_Complete_12Plus                 <dbl> 141688, 36383, 1091967, 872683,…
## $ Series_Complete_12PlusPop_Pct          <dbl> 0.0, 95.0, 90.2, 0.0, 95.0, 95.…
## $ vxcgte18                               <dbl> 141688, 29946, 1009600, 792582,…
## $ vxcgte18pct                            <dbl> 0.0, 95.0, 90.5, 0.0, 95.0, 95.…
## $ vxcgte65                               <dbl> 3819, 2994, 257765, 148580, 200…
## $ vxcgte65pct                            <dbl> 0.0, 91.3, 95.0, 0.0, 95.0, 95.…
## $ Series_Complete_Janssen                <dbl> 14150, 585, 66365, 39327, 61635…
## $ Series_Complete_Moderna                <dbl> 57123, 9540, 380958, 410201, 31…
## $ Series_Complete_Pfizer                 <dbl> 70416, 31720, 704866, 468368, 5…
## $ Series_Complete_Novavax                <dbl> 0, 0, 133, 18, 125, 86, 630, 32…
## $ Series_Complete_Unk_Manuf              <dbl> 0, 634, 354, 73, 777, 1438, 559…
## $ Series_Complete_Janssen_5Plus          <dbl> 14149, 585, 66337, 39300, 61634…
## $ Series_Complete_Moderna_5Plus          <dbl> 57123, 9540, 379050, 409220, 30…
## $ Series_Complete_Pfizer_5Plus           <dbl> 70416, 31719, 703282, 467783, 5…
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 0, 634, 353, 73, 760, 1411, 550…
## $ Series_Complete_Janssen_12Plus         <dbl> 14149, 585, 66334, 39299, 61634…
## $ Series_Complete_Moderna_12Plus         <dbl> 57123, 9539, 378746, 408993, 30…
## $ Series_Complete_Pfizer_12Plus          <dbl> 70416, 25626, 646452, 424304, 5…
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 0, 633, 304, 69, 716, 1139, 526…
## $ Series_Complete_Janssen_18Plus         <dbl> 14149, 584, 66144, 39254, 61604…
## $ Series_Complete_Moderna_18Plus         <dbl> 57123, 9516, 377810, 408550, 30…
## $ Series_Complete_Pfizer_18Plus          <dbl> 70416, 19217, 565253, 344693, 4…
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 0, 629, 264, 69, 667, 842, 499,…
## $ Series_Complete_Janssen_65Plus         <dbl> 201, 28, 11854, 3508, 6866, 321…
## $ Series_Complete_Moderna_65Plus         <dbl> 1837, 1217, 113726, 84001, 8940…
## $ Series_Complete_Pfizer_65Plus          <dbl> 1781, 1732, 132111, 61063, 1040…
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 0, 17, 36, 5, 183, 219, 97, 128…
## $ Additional_Doses                       <dbl> 53622, 24680, 690868, 345445, 5…
## $ Additional_Doses_Vax_Pct               <dbl> 37.8, 58.1, 59.8, 37.6, 58.4, 5…
## $ Additional_Doses_5Plus                 <dbl> 53622, 24680, 690646, 345311, 5…
## $ Additional_Doses_5Plus_Vax_Pct         <dbl> 37.8, 58.1, 60.1, 37.7, 58.7, 5…
## $ Additional_Doses_12Plus                <dbl> 53622, 24648, 676291, 335147, 5…
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 37.8, 67.7, 61.9, 38.4, 60.6, 5…
## $ Additional_Doses_18Plus                <dbl> 53622, 21198, 641485, 310374, 5…
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 37.8, 70.8, 63.5, 39.2, 62.0, 5…
## $ Additional_Doses_50Plus                <dbl> 16806, 8437, 396324, 182866, 30…
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 54.9, 79.6, 77.4, 47.7, 73.5, 6…
## $ Additional_Doses_65Plus                <dbl> 2697, 2412, 218746, 82374, 1627…
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 70.6, 80.6, 84.9, 55.4, 81.1, 7…
## $ Additional_Doses_Moderna               <dbl> 31606, 4945, 288011, 154519, 22…
## $ Additional_Doses_Pfizer                <dbl> 21350, 19729, 396026, 188102, 3…
## $ Additional_Doses_Janssen               <dbl> 643, 2, 6539, 2525, 5352, 2113,…
## $ Additional_Doses_Unk_Manuf             <dbl> 23, 4, 245, 294, 210, 501, 247,…
## $ Second_Booster                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus                  <dbl> 5026, 2274, 239196, 80425, 1721…
## $ Second_Booster_50Plus_Vax_Pct          <dbl> 29.9, 27.0, 60.4, 44.0, 55.8, 5…
## $ Second_Booster_65Plus                  <dbl> 1115, 875, 149385, 41110, 10615…
## $ Second_Booster_65Plus_Vax_Pct          <dbl> 41.3, 36.3, 68.3, 49.9, 65.2, 6…
## $ Second_Booster_Janssen                 <dbl> 3, 0, 123, 21, 178, 93, 196, 26…
## $ Second_Booster_Moderna                 <dbl> 2634, 532, 144967, 48240, 10103…
## $ Second_Booster_Pfizer                  <dbl> 6153, 1907, 161732, 65694, 1262…
## $ Second_Booster_Unk_Manuf               <dbl> 3, 0, 54, 256, 108, 373, 61, 22…
## $ Administered_Bivalent                  <dbl> 9837, 525, 311091, 169098, 2565…
## $ Admin_Bivalent_PFR                     <dbl> 9507, 328, 169384, 107715, 1438…
## $ Admin_Bivalent_MOD                     <dbl> 330, 197, 141707, 61383, 112675…
## $ Dist_Bivalent_PFR                      <dbl> 19560, 1610, 357640, 321450, 29…
## $ Dist_Bivalent_MOD                      <dbl> 11400, 500, 245420, 162380, 189…
## $ Bivalent_Booster_5Plus                 <dbl> 9773, 563, 312151, 168051, 2664…
## $ Bivalent_Booster_5Plus_Pop_Pct         <dbl> 0.0, 1.3, 23.5, 0.0, 26.5, 33.4…
## $ Bivalent_Booster_12Plus                <dbl> 9773, 560, 304879, 161297, 2607…
## $ Bivalent_Booster_12Plus_Pop_Pct        <dbl> 0.0, 1.5, 25.2, 0.0, 28.1, 34.9…
## $ Bivalent_Booster_18Plus                <dbl> 9773, 555, 294343, 149113, 2519…
## $ Bivalent_Booster_18Plus_Pop_Pct        <dbl> 0.0, 1.8, 26.4, 0.0, 29.5, 35.6…
## $ Bivalent_Booster_65Plus                <dbl> 677, 197, 138250, 45820, 109055…
## $ Bivalent_Booster_65Plus_Pop_Pct        <dbl> 0.0, 6.0, 51.5, 0.0, 58.3, 55.7…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
##   isType tot_cases tot_deaths new_cases    new_deaths        n
##   <chr>      <dbl>      <dbl>     <dbl>         <dbl>    <dbl>
## 1 before   8.02e+9    1.06e+8   1.04e+8 1121270       9853    
## 2 after    7.95e+9    1.05e+8   1.03e+8 1114704       8517    
## 3 pctchg   8.63e-3    4.92e-3   1.21e-2       0.00586    0.136

## Warning: Using `all_of()` outside of a selecting function was deprecated in tidyselect
## 1.2.0.
## ℹ See details at
##   <https://tidyselect.r-lib.org/reference/faq-selection-context.html>

## Warning: Using `by = character()` to perform a cross join was deprecated in dplyr 1.1.0.
## ℹ Please use `cross_join()` instead.

## 
## Processed for cdcWeeklyBurden:
## Rows: 59,313
## Columns: 6
## $ date       <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26…
## $ state      <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK",…
## $ tot_cases  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tot_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_cases  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 5.76e+7    5.07e+7 1511938      60599     
## 2 after  5.73e+7    5.04e+7 1483681      57789     
## 3 pctchg 5.37e-3    5.12e-3       0.0187     0.0464
## 
## 
## Processed for cdcHosp:
## Rows: 57,789
## Columns: 5
## $ date       <date> 2021-02-18, 2021-02-13, 2021-02-05, 2021-02-02, 2021-01-26…
## $ state      <chr> "SD", "NM", "CT", "MA", "ME", "MD", "RI", "MT", "DE", "ND",…
## $ inp        <dbl> 90, 334, 979, 1518, 221, 2180, 423, 181, 510, 162, 434, 449…
## $ hosp_adult <dbl> 87, 327, 973, 1501, 220, 2161, 416, 178, 501, 159, 432, 446…
## $ hosp_ped   <dbl> 3, 7, 6, 17, 1, 19, 7, 3, 9, 3, 2, 3, 24, 10, 73, 15, 5, 4,…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…²       n
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
## 1 before 4.54e+11 1.84e+11 1636331.    4.63e+10 2.40e+6 1.70e+11 1.92e+6 3.81e+4
## 2 after  2.19e+11 8.89e+10 1368749.    2.24e+10 2.12e+6 8.21e+10 1.63e+6 3.01e+4
## 3 pctchg 5.18e- 1 5.16e- 1       0.164 5.16e- 1 1.16e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹vxcgte65pct, ²vxcgte18pct
## 
## 
## Processed for vax:
## Rows: 42,636
## Columns: 9
## $ date        <date> 2020-12-14, 2020-12-15, 2020-12-16, 2020-12-17, 2020-12-1…
## $ state       <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"…
## $ vxa         <dbl> 0, 0, 0, 2, 2, 1607, 4239, 5125, 5615, 6822, 8578, 10612, …
## $ vxc         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcpoppct   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte65pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ vxcgte18pct <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## 
## Integrated per capita data file:
## Rows: 59,679
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.

saveToRDS(cdc_daily_230402, ovrWriteError=FALSE)

## 
## File already exists: ./RInputFiles/Coronavirus/cdc_daily_230402.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE

## NULL

The latest hospitalization data is also downloaded and processed:

# Run for latest data, save as RDS
indivHosp_20230403 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20230403.csv")

## 
## File ./RInputFiles/Coronavirus/HHS_Hospital_20230403.csv already exists
## File will not be downloaded since ovrWrite is not TRUE

## Rows: 261207 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl  (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl    (2): is_metro_micro, is_corrected
## date   (1): collection_week
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## Rows: 261,207
## Columns: 128
## $ hospital_pk                                                                        <chr> …
## $ collection_week                                                                    <date> …
## $ state                                                                              <chr> …
## $ ccn                                                                                <chr> …
## $ hospital_name                                                                      <chr> …
## $ address                                                                            <chr> …
## $ city                                                                               <chr> …
## $ zip                                                                                <chr> …
## $ hospital_subtype                                                                   <chr> …
## $ fips_code                                                                          <chr> …
## $ is_metro_micro                                                                     <lgl> …
## $ total_beds_7_day_avg                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> …
## $ inpatient_beds_used_7_day_avg                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> …
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> …
## $ inpatient_beds_7_day_avg                                                           <dbl> …
## $ total_icu_beds_7_day_avg                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> …
## $ icu_beds_used_7_day_avg                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> …
## $ total_beds_7_day_sum                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> …
## $ inpatient_beds_used_7_day_sum                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> …
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> …
## $ inpatient_beds_7_day_sum                                                           <dbl> …
## $ total_icu_beds_7_day_sum                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> …
## $ icu_beds_used_7_day_sum                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> …
## $ total_beds_7_day_coverage                                                          <dbl> …
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> …
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> …
## $ inpatient_beds_7_day_coverage                                                      <dbl> …
## $ total_icu_beds_7_day_coverage                                                      <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> …
## $ icu_beds_used_7_day_coverage                                                       <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> …
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> …
## $ geocoded_hospital_address                                                          <chr> …
## $ hhs_ids                                                                            <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> …
## $ is_corrected                                                                       <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg                                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage                                <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum                                     <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg                                             <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage                                        <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum                                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum                   <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum                    <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum                 <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg                           <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage                      <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum                           <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg                                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage                                 <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum                                      <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg                                         <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage                                    <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum                                         <dbl> …
## 
## Hospital Subtype Counts:
## # A tibble: 4 × 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         4857
## 2 Critical Access Hospitals  70233
## 3 Long Term                  17536
## 4 Short Term                168581
## 
## Records other than 50 states and DC
## # A tibble: 5 × 2
##   state     n
##   <chr> <int>
## 1 AS       29
## 2 GU      123
## 3 MP       40
## 4 PR     2556
## 5 VI       98
## 
## Record types for key metrics

## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = name`.

## # A tibble: 10 × 5
##    name                                              `NA` Posit…¹ Value…²  Total
##    <chr>                                            <int>   <int>   <int>  <int>
##  1 all_adult_hospital_beds_7_day_avg                77420  183398     389 261207
##  2 all_adult_hospital_inpatient_bed_occupied_7_day… 30117  211729   19361 261207
##  3 icu_beds_used_7_day_avg                          33204  199916   28087 261207
##  4 inpatient_beds_7_day_avg                          8352  251849    1006 261207
##  5 inpatient_beds_used_7_day_avg                     8352  231713   21142 261207
##  6 inpatient_beds_used_covid_7_day_avg               1763  173895   85549 261207
##  7 staffed_icu_adult_patients_confirmed_and_suspec… 30086  156464   74657 261207
##  8 total_adult_patients_hospitalized_confirmed_and… 27571  157031   76605 261207
##  9 total_beds_7_day_avg                             53399  207570     238 261207
## 10 total_icu_beds_7_day_avg                          3330  244695   13182 261207
## # … with abbreviated variable names ¹Positive, ²`Value -999999`

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20230403, ovrWriteError=FALSE)

## 
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20230403.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE

## NULL

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_230402 <- postProcessCDCDaily(cdc_daily_230402, 
                                              dataThruLabel="Mar 2023", 
                                              keyDatesBurden=c("2023-03-29", "2022-06-30", 
                                                               "2021-12-31", "2021-06-30"
                                                               ),
                                              keyDatesVaccine=c("2023-03-29", "2021-12-31", 
                                                                "2021-08-31", "2021-03-31"
                                                                ), 
                                              returnData=TRUE
                                              )

## Joining with `by = join_by(state)`

## 
## *** File has been checked for uniqueness by: state date name

## Warning: Removed 24 rows containing missing values (`geom_line()`).

## Warning: Removed 24 rows containing missing values (`position_stack()`).

## Warning: Removed 24 rows containing missing values (`position_stack()`).

## Warning: Removed 9 rows containing missing values (`geom_line()`).

## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.

## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.

## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.

## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.

## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.

## Warning: Specifying the `id_cols` argument by position was deprecated in tidyr 1.3.0.
## ℹ Please explicitly name `id_cols`, like `id_cols = state`.

# Create hospitalized per capita data
hospPerCap_230402 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_230402, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )

## Warning: Removed 18 rows containing missing values (`geom_line()`).

burdenPivotList_230402$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")

## # A tibble: 18 × 6
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con… 5.13e4 60599
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con… 3.20e5 60599
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con… 4.59e5 60599
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con… 5.40e5 60599
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con… 8.69e5 60599
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con… 1.18e6 60599
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con… 1.23e6 60599
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con… 1.17e6 60599
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus… 4.47e4 60599
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus… 2.98e5 60599
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus… 3.93e5 60599
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus… 3.98e5 60599
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus… 6.32e5 60599
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus… 8.86e5 60599
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus… 8.73e5 60599
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus… 8.03e5 60599
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid… 2.03e5 60599
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid… 4.75e5 60599

saveToRDS(burdenPivotList_230402, ovrWriteError=FALSE)

## 
## File already exists: ./RInputFiles/Coronavirus/burdenPivotList_230402.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE

## NULL

saveToRDS(hospPerCap_230402, ovrWriteError=FALSE)

## 
## File already exists: ./RInputFiles/Coronavirus/hospPerCap_230402.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE

## NULL

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_230402)

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 20 rows containing missing values (`geom_line()`).

## Warning: Removed 20 rows containing missing values (`geom_line()`).

## # A tibble: 10,236 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 10,226 more rows

Hospital data are pieced together as needed:

# Create modified hospital data
multiSourceHosp_20230402 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
                                                        readFromRDS("indivHosp_20230403")
                                                        ),
                                                   timeVec=as.Date("2022-01-01")
                                                   )

The updated hospital data are then plotted:

# Run hospital plots
modStateHosp_20230402 <- hospitalCapacityCDCDaily(multiSourceHosp_20230402, 
                                                  plotSub="Aug 2020 to Mar 2023\nOld data used pre-2022"
                                                  )

Data Refreshes

Data From 2023-05-02